A multimodal study regarding neural correlates of the subjective well-being in healthy individuals

Although happiness or subjective well-being (SWB) has drawn much attention from researchers, the precise neural structural correlates of SWB are generally unknown. In the present study, we aimed to investigate the associations between gray matter (GM) volumes, white matter (WM) microstructures, and SWB in healthy individuals, mainly young adults using multimodal T1 and diffusion tensor imaging studies. We enrolled 70 healthy individuals using magnetic resonance imaging. We measured their SWB using the Concise Measure of Subjective Well-Being. Voxel-wise statistical analysis of GM volumes was performed using voxel-based morphometry, while fractional anisotropy (FA) values were analyzed using tract-based spatial statistics. In healthy individuals, higher levels of SWB were significantly correlated with increased GM volumes of the anterior insula and decreased FA values in clusters of the body of the corpus callosum, precuneus WM, and fornix cres/stria terminalis. A correlational analysis revealed that GM volumes and FA values in these significant regions were significantly correlated with severity of psychological symptoms such as depression, anxiety, and quality of life. Our findings indicate that GM volumes and WM microstructures in these regions may contribute to SWB, and could be the neural basis for psychological symptom severity as well as quality of life in healthy individuals.

Happiness, or subjective well-being (SWB), is a multidimensional construct that concerns optimal experience and functioning. It has several components, including a cognitive aspect of life satisfaction, an affective aspect of the presence of positive emotions and mood, and the absence or reduction of negative emotions and mood, together often summarized as happiness 1 .
SWB plays a considerable role in enhancing individuals' quality of life, such as success in psychological health, work, and interpersonal relationships. Low SWB acts as a risk factor for depression 2 as well as anxiety 3 , and poor mental health 4 . Individuals reporting greater levels of SWB showed several positive aspects of cognitive and motivational processes, such as encoding relatively more positive than negative events into memory, perceiving and interpreting life circumstances in positive ways, being less sensitive to negative feedback, and excessive selffocused cognition (rumination) 5,6 . Additionally, higher SWB was associated with stronger emotional regulation abilities, resilience to stress, and mindfulness 7 . These characteristics affect how individuals manage stressful situations and respond effectively by enhancing well-being 8 . Furthermore, a review study indicated that SWB is associated with cognitive control processes and a flexible brain network that responds to perpetually shifting contextual task demands 9 .
Several functional brain magnetic resonance imaging (fMRI) studies have reported that default mode network (DMN) and salience network may have implications for SWB. fMRI studies have shown that individuals with higher SWB show less activation and decreased functional connectivity in the default mode network (DMN) nodes, such as the precuneus and posterior cingulate cortex, which indicates a flexible brain network 10,11 . Hence, reduced functional connectivity is correlated with less rumination and flexible affective processing. Furthermore, evidence has shown a positive correlation between social well-being and the fractional amplitude of low-frequency fluctuations in the salience network including the right anterior cingulate cortex and insula 12  www.nature.com/scientificreports/ Another functional neuroimaging study reported that SWB negatively correlates with functional connectivity between the salience network and DMN 13 . Structurally, previous MRI studies have consistently found that SWB in individuals is related to brain areas associated with emotional regulation, specifically, the insula 14 , anterior cingulate cortex 15,16 , dorsolateral prefrontal cortex 17 , and orbitofrontal cortex 18 . In particular, a previous MRI study indicated that healthy individuals with larger gray matter (GM) volumes in the insula were correlated with eudaimonic well-being and polygenic scores of SWB 19,20 . However, whether and how general SWB-related GM and white matter (WM) brain regions are related to other psychological characteristics, such as quality of life and psychiatric symptoms, has never been investigated in healthy individuals. In addition, the neural structural correlates of SWB in multimodal neuroimaging studies and the relationship between WM and GM regions related to SWB are not yet known. Taken together, these results suggest that elevated SWB among healthy individuals may be related to brain regions associated with the DMN and emotional regulation. Therefore, it is needed to investigate whether GM volumes and WM microstructures can be associated with SWB, as well as to reveal their inter-relationships among the neural correlates of GM and WM regions significantly correlated with SWB in healthy individuals.
In the present study, we investigated neural correlates such as GM volumes and WM microstructures related to SWB, three SWB subscales, and their associations with the severity of symptoms and mental health in quality of life in healthy individuals. We also examined the inter-relationships among neural correlates of the GM and WM regions that were significantly associated with SWB. We hypothesized that (1) GM volumes and WM microstructures in the insula and DMN-related brain WM regions in healthy individuals would be related to SWB, (2) there could be a significant association between SWB-related GM and WM regions and the severity of symptoms such as depression, anxiety, and quality of life, and (3) there would be an inter-relationship among neural correlates of GM and WM regions significantly associated with SWB.

Results
Demographic and clinical characteristics of study participants. Table 1 summarizes the demographic and clinical characteristics of the study participants.
Associations between subjective well-being and the gray matter volumes in healthy individuals. Whole brain analysis of voxel-based morphometry (VBM) showed a significant positive correlation between GM volumes in the anterior insula (AI) and the Concise Measure of Subjective Well-Being (COMO-SWB) total scores (p family-wise error (FWE) = 0.017 at the cluster level) (Fig. 1). After the analysis of covariance for age, sex, and total intracranial volume (TIV), the association remained significant. In addition, life satisfaction subscale scores were positively correlated with AI (p FWE = 0.02, at the cluster level). There were no significant associations between gray matter volume and other positive and negative emotion subscale scores.

Marital status
Living with partner (n) 29 Living without partner (n) 28

Job
Existed (  Voxel-wise correlation between gray matter volumes and white matter microstructures associated with subjective well-being. The adjusted GM volume values in the AI were significantly negatively correlated (TFCE-corrected p < 0.05) with the FA values in the significant clusters of the body of the CC (Fig. 4). No significant correlations were found between the adjusted GM volume values in the AI and other significant WM clusters such as FX/ST and precuneus WM.

Discussion
To the best of our knowledge, this is the first multimodal T1 and diffusion tensor imaging (DTI) study to reveal the relationship between SWB and GM volumes in the AI and WM microstructures in the FX/ST, precuneus WM, and body of the CC in healthy individuals. In particular, further analysis revealed that the GM volumes  www.nature.com/scientificreports/ in the AI were correlated with the FAs in the body of the CC, which may show the inter-relationships between WM and GM for contributing to SWB. We demonstrated that higher GM volume values in the AI and lower FA values in the FX/ST, precuneus WM, and body of the CC were correlated with higher SWB in healthy individuals. Furthermore, adjusted GM volume and FA values in these significant regions were significantly associated with quality of life and severity of symptoms, such as depression and anxiety. The present study found that, among healthy individuals, higher GM volume values in the AI were related to higher COMOSWB total scores. Although in several previous neuroimaging studies, the insula was mainly considered as a region functionally and anatomically related to only social and eudaimonic wellbeing, not general SWB 12,19 , relationship between the total scores of COMOSWB, which reflects general SWB and GM volumes of the AI, has not been shown yet. AI, which has reciprocal connections to limbic regions, plays a crucial role in interoception and emotional awareness 21 . According to a functional neuroimaging study, AI, a crucial region in salience network integrates information from cognitive, emotional, and affective processes 14 . Hence, AI supports subjective feeling states and regulates the introduction of subjective feelings into cognitive and motivational processes 22 . Our findings may indicate that accurate interoception and integration of emotional and affective processes from increased GM volumes in the AI is a critical component of SWB.
Furthermore, our findings revealed a positive correlation between GM volume in the AI and the life satisfaction subscale scores of the COMOSWB. Life satisfaction is an essential component of SWB and can influence a global judgment of quality of life according to an individual's own needs and expectations 23 . Higher life satisfaction reflects greater flexibility, more positive self-cognition, relative insensitivity to negative stimuli, and reaction to more positive contexts 24 . Our findings are partly supported by previous studies on diffusion microstructure reporting significant associations between the frontoinsula and life satisfaction 23,25 This result implies that life satisfaction, the components of SWB caused by regulating of positive stimuli and emotions, are associated with increased GM volumes in the AI.
We also revealed negative correlations between adjusted GM volumes in the AI correlated with SWB and the total BDI-II and BAI scores. Subsequently, we found that the adjusted GM volumes in the AI were positively correlated with the psychological health subscale scores of the WHOQOL. Our findings are consistent with those of previous studies reporting AI to be associated with quality of life and the severity of symptoms such as depression, anxiety 26,27 . Taken together, less severe symptoms and a higher quality of life could be associated with higher SWB in healthy individuals.
The findings of our study suggest that lower mean FA values in the body of the CC, FX/ST, and precuneus WM were associated with higher COMOSWB total scores. FA, which represents WM microstructures, is a sensitive measure of the degree of water diffusion along the WM tracts 28 . Our results are partly in line with a prior study indicating that meditators with improved emotional regulation exhibited relatively less activation in the hippocampal formations, amygdala, and primary nodes of the DMN 29 . Furthermore, functional connectivity www.nature.com/scientificreports/ in the DMN regions was correlated with reduced rumination 30 , which can lead to higher SWB. These previous studies support our results, reporting that higher levels of SWB could be characterized by decreased WM connectivity in the FX/ST related to the hippocampal formations and amygdala 31,32 , and the CC and precuneus related to the DMN 33 . The CC, the largest fiber bundle connecting the two cerebral hemispheres, is widely regarded as an important region responsible for interhemispheric integration and information processing of input and output signals to facilitate coordination of thoughts and behaviors 34,35 . In a previous study, interhemispheric inhibition mediated by the CC was related to emotional functioning, such as regulating emotions and recognizing emotional state 36 . Although CC has not been related to SWB in previous neuroimaging studies, CC was found to be crucial for resilience as an important predictor for enhancing SWB 37 . Functionally, lower FA values in the body of the CC may lead to enhanced emotional functioning related to SWB in healthy individuals.
The FX is an important WM tract bundle that is located underneath the CC, connects the hippocampus and septum, and plays a crucial role in the formation and consolidation of declarative memories 31 . FX is a part of the Papez circuit, which is an important structure of the limbic system and is involved in the regulation of emotions by higher-order frontal cortical brain regions 38 . The ST is the WM tract bundle, which is thought to connect the septum and amygdala and participates in cognition and emotional regulation with the FX and cingulum bundle from the limbic system 32 . As demonstrated by our findings, adaptive cognition and emotional regulation by the microstructure of FX/ST may be associated with higher SWB.
The functional core of the DMN 39 , the precuneus, is part of the flexible affective workspace and is functionally and structurally connected to the medial prefrontal cortex and posterior cingulate cortex in the DMN 40,41 . A previous study reported a positive relationship between FA and functional connectivity in the DMN in healthy subjects 42 . These studies have shown that precuneus WM affects functional connectivity in the DMN. Our findings are a continuation of a previous functional neuroimaging-based study 10 indicating that higher levels of SWB lead to a stable DMN. Thus, hyperconnectivity and hyperactivation of the DMN are associated with higher levels of rumination about self-feelings, thoughts, and emotions, which leads to lower levels of SWB 10 . Moreover, the precuneus is considered to play a crucial role in integrating different types of information and converting it into subjective happiness in a previous study regarding the precuneus as the structural correlate of happiness in healthy individuals 43 . Our findings suggest that decreased FAs in lower levels of rumination and adaptive integration of internal and external information related to emotional regulation caused by reduced FAs in the precuneus may contribute to higher SWB.
In the subscale analysis, we also found a significant correlation between the positive emotion subscale scores and the FA values of the posterior corona radiata, superior longitudinal fasciculus, and splenium of the CC. Experiencing positive emotions benefits psychological and physical well-being in intersecting ways, including modulating neurophysiological correlates within the central and peripheral nervous systems, and is associated with the brain networks implicated in cognitive control and flexible affective processing 9 . The posterior corona radiata, superior longitudinal fasciculus, and splenium of the CC are included in the main WM regions related to the DMN as part of the flexible brain network 33 . Our findings indicate that decreased FA values in these regions related to the DMN may affect positive emotions through flexible affective processes and adaptive cognitive control.
Furthermore, our results demonstrated that WM microstructures in the FX/ST and precuneus related to SWB was positively correlated with the severity of psychological symptoms such as depression and anxiety. BAI scores were correlated with the extracted FA values of the FX/ST and precuneus WM. Moreover, BDI-II scores were positively correlated with the precuneus WM. These results are consistent with the observations in previous studies showing that the precuneus is associated with the pathophysiology of depression and anxiety 44 and that the FX/ST microalterations may be related to the septum's effect on anxiety regulation 45 . Our findings thus indicate that WM microstructures in the FX/ST and precuneus WM contribute to the lower severity of psychological symptoms.
Interestingly, correlation analysis showed that the decreased FA values in the body of the CC, FX/ST, and precuneus WM related to SWB were associated with the social and psychological health subscale scores of WHOQOL. We observed a negative relationship between FA values in the body of the CC, FX/ST, and precuneus WM and psychological health, as well as between the FX/ST and precuneus WM and social health. This is partly supported by a previous study reporting that DMN was associated with psychological and social health in terms of quality of life 46 . In the present study, the association was replicated only in the body of the CC, FX/ST, and the precuneus WM related to DMN, which are more specific areas than those in previous studies.
In addition, our further analysis revealed that the GM volumes in the AI were correlated with the FAs in the body of the CC related to SWB. This finding supports a previous study showing that AI is connected with CC 47 . Our findings also imply that GM in the AI and WM in the CC can play an important role, interacting with each other in the generation of SWB.
This study has a few limitations that should be interpreted with caution. First, since the sample size was relatively small, the association between SWB and the insula, body of CC, FX/ST, and precuneus WM should be confirmed using a larger sample. Second, all measures were based on self-report, which relied on the awareness of individuals, and the results may be affected by response bias. Nevertheless, all assessments were objective indicators that had already been repeatedly used and verified.
In conclusion, our study demonstrated increased GM volumes in the AI and decreased WM connectivity of the body of the CC, FX/ST, and precuneus in healthy individuals with higher SWB, its association with symptom severity and quality of life, and the inter-relationship between GM volume and WM microstructure. The present study results suggest that these regions can be considered potential predictive markers for SWB, as they are replicated repeatedly and the significance between symptoms is verified. Assessments. SWB was assessed using the Korean version of the COMOSWB, which revealed high reliability (Cronbach's α = 0.86) 49 and a high value for evaluating general SWB. The survey consists of nine self-reporting questionnaires assessing the following three domains: life satisfaction, which evaluates personal, relational, and collective life satisfaction; positive emotion and negative emotion, which consist of items representing high, medium, and low levels of emotional arousal, and each domain consists of three questionnaires. It was rated on a scale ranging from strongly disagree (1) to strongly agree (7) during the past month. The total scores were defined by subtracting negative emotion subscale scores from the sum of life satisfaction subscale scores and positive emotion subscale scores and range from − 15 to 39.
To evaluate the severity of anxiety and depressive symptoms in participants, the BAI 50 and BDI-II 51 were used. The scales showed high internal consistency (Cronbach's α = 0.92 for BAI and Cronbach's α = 0.91 for BDI-II) 52,53 . Additionally, the WHOQOL-BREF was administered to assess the participants' quality of life. The WHOQOL-BREF contains 24 items in the four domains of physical, psychological, social, and environmental health. In addition, it has two facets: evaluating the overall quality of life and general health. The instrument domains have high internal consistency (Cronbach's α = 0.90) and a validity 54 . The raw scores were calculated and converted into transformed scores.
Neuroimaging data acquisition. All participants underwent brain MRI at baseline using a 3.0-Tesla GE Signa HDxt scanner (GE Healthcare, Milwaukee, WI, USA), which consisted of an 8-channel phase-array head coil. All MRI scans were acquired at the CHA Bundang Medical Center at CHA University.
Diffusion-weighted imaging (DWI) were acquired using an echo planar imaging (EPI) sequence using the following parameters: repetition time, 17,000 ms; echo time, 108 ms; field of view, 240 mm; acquisition matrix, 144 × 144; slice thickness, 1.7 mm; and voxel size, 1.67 × 1.67 × 1.7 mm 3 . A double-echo option was implemented to reduce the eddy current-related distortions. To reduce the effects of EPI spatial distortions, an 8-channel coil and an array of spatial sensitivities encoding a speed-up factor of 2 was used. A total of 70 axial slices parallel to the anterior commissure-posterior commissure line encompassing the entire brain in 51 directions with b = 900 s/ mm 2 was obtained, as well as 8 baseline scans with b 0 = 0 s/mm 2 .
VBM image processing and analysis. Voxel-based morphometry (VBM) 55 was performed using Statistical Parametric Mapping 12 (SPM12, http:// www. fil. ion. ucl. ac. uk/ spm) and the Computational Anatomy Toolbox 12 (CAT12, http:// www. neuro. uni-jena. de/ cat/) implemented in MATLAB R2021a (Mathworks). After reorienting the T1-weighed images to define the anterior commissure as the origin, the T1-weighed images were segmented into GM, WM, and cerebrospinal fluid and normalized into a Montreal Neurologic Institute (MNI) space using CAT12. All normalized and modulated GM volumes were smoothed using an isotropic Gaussian kernel with a full width at half maximum of 6 mm. An absolute threshold masking of 0.1 was applied to restrict only voxels of gray matter volumes.
A whole-brain multiple regression analysis was done using the total scores of the COMOSWB as an independent variable and the TIV calculated using CAT12 as a covariate. Additionally, to control for possible confounding variables, age and sex were controlled for covariates. The significance threshold was set at a cluster-level of p < 0.05, FWE corrected. For further correlational analysis, the MarsBar tool box 56 was used to extract mean gray matter volumes in a cluster that showed a significant correlation with the COMOSWB total scores for each participant.
DTI image processing and analysis. The least-squares method was used with the Functional MRI of the Brain (FMRIB) Software Library (FSL; version 5.0; Oxford, UK; https:// fsl. fmrib. ox. ac. uk/ fsl/) to extract DTIs from DWI (approximate scan time, 17 min). All images were visually inspected for quality by an imaging specialist, and samples with no significant movement or other artifacts (e.g., warping) were chosen.
A whole-brain voxel-wise statistical analysis of fractional anisotropy (FA) data was performed using Tract-Based Spatial Statistics (TBSS) version 1.2, as provided in the FSL, according to the standard procedure 57  www.nature.com/scientificreports/ on the skeletonized FA data using FSL Randomize version 2.1. General linear model regression analysis was performed, with 5000 random permutations, and the significance level was set at p < 0.05, corrected for the FWE rate. For further analysis, age and sex were controlled for as covariates. To avoid making an arbitrary choice of the cluster-forming threshold, a multiple comparison correction using threshold-free cluster enhancement (TFCE) was used, while preserving the sensitivity benefits of cluster-wise correction 58 . For further correlation analysis, the mean FA values of clusters that showed significant correlations with the total scores of the COMOSWB in the TBSS analysis were obtained using 3D slicer version 3.6 59 . In addition, voxel-wise statistical analysis further demonstrated the inter-relationship between significant clusters of the GM and WM using the TBSS. That is, after extracting the adjusted volume values of the significant GM regions correlated with SWB, TBSS voxel-wise correlation analysis was applied to explore the significant extracted clusters of the GM and their WM correlates.
Statistical analysis. Pearson correlation analysis was performed to determine the relationship between WM FA values and GM volume extracted values of the significant clusters and other continuous variable such as the BAI, BDI-II, and WHOQOL-BREF. All statistical analyses except for voxel-wise analyses were performed using Statistical Package for the Social Sciences version 26.0 (IBM Corporation, Armonk, NY, USA). To solve the multiple comparison problem, an FDR correction was performed (q < 0.05).
Ethical approval. All study procedures were reviewed and approved by the Institutional Review Board of CHA Bundang Medical Center in accordance with the latest version of the Declaration of Helsinki and principles of Good Clinical Practice.
Informed consent. Informed consent was obtained from all individual participants included in the study.

Data availability
The datasets generated and analyzed during the current study are not publicly available due to legal or ethical restrictions that protect patients' privacy and concent but available from the corresponding author on reasonable request.